multi_predict() for coxnet models

[hfrick]

This PR implements multi_predict() for coxnet models, for types "survival" and "linear_pred".

It's the missing part to #33.

library(censored)
#> Loading required package: parsnip
library(survival)

spec_g <- proportional_hazards(penalty = 0.123) %>% 
  set_engine("glmnet")

fit_g <- fit(spec_g, 
             Surv(stop, event) ~ rx + size + number + strata(enum),
             data = bladder)

new_data_3 <- bladder[1:3, ]

mp_s <- multi_predict(fit_g, new_data_3, penalty = c(0.05, 0.1),
                    type = "survival", time = c(5, 10))
mp_s
#> # A tibble: 3 x 1
#>   .pred           
#>   <list>          
#> 1 <tibble [4 × 3]>
#> 2 <tibble [4 × 3]>
#> 3 <tibble [4 × 3]>
tidyr::unnest(mp_s, cols = .pred)
#> # A tibble: 12 x 3
#>    penalty .time .pred_survival
#>      <dbl> <dbl>          <dbl>
#>  1    0.05     5          0.727
#>  2    0.1      5          0.721
#>  3    0.05    10          0.628
#>  4    0.1     10          0.623
#>  5    0.05     5          0.989
#>  6    0.1      5          0.989
#>  7    0.05    10          0.931
#>  8    0.1     10          0.929
#>  9    0.05     5          0.989
#> 10    0.1      5          0.988
#> 11    0.05    10          0.966
#> 12    0.1     10          0.964

mp_lp <- multi_predict(fit_g, new_data_3, penalty = c(0.05, 0.1),
                       type = "linear_pred")
mp_lp
#> # A tibble: 3 x 1
#>   .pred           
#>   <list>          
#> 1 <tibble [2 × 2]>
#> 2 <tibble [2 × 2]>
#> 3 <tibble [2 × 2]>
tidyr::unnest(mp_lp, cols = .pred)
#> # A tibble: 6 x 2
#>   penalty .pred_linear_pred
#>     <dbl>             <dbl>
#> 1    0.05           -0.0701
#> 2    0.1             0.0461
#> 3    0.05           -0.0701
#> 4    0.1             0.0461
#> 5    0.05           -0.0701
#> 6    0.1             0.0461

^{Created on 2021-06-28 by the reprex package (v2.0.0)}

[hfrick]

There are currently predict methods for types "linear_pred" and "survival". For the "linear_pred"-type predictions, this follows what parsnip does for linear_reg() with a glmnet engine. For the survival probabilities, we use the survival curves from survfit() and have the wrapper survival_prob_coxnet() already so I extended that one to be able to deal with a vector of penalties. This also allows for convenient minimal nesting where we only group according to strata, see also #47 and #63.

[DavisVaughan]

I've reviewed to the best of my ability (more on the technical R side than the statistical side), so it will definitely be nice to have Emil review as well! But looks great!

+1 for adding PR comments to your own PR, it was really nice to have somewhere to add extra relevant comments

[DavisVaughan]

Since this else statement is pretty long, you might consider making two helpers for the specific predict types and doing:

switch(
  type,
  linear_pred = multi_predict_coxnet_linear_pred(...),
  survival = multi_predict_coxnet_survival(...),
  abort("Internal error: Unknown `type`.")
)

That would also make it easier to extend if we get more types

[hfrick]

I've moved the code for linear_pred into its helper function 👍 Regarding the switch statement: I think the other types may require a similar structure as the survival probabilities so hopefully the rest is just predict(type = type).

[EmilHvitfeldt]

Everything looks correct to my eyes, well done!
I had a small note about possible performance improvements, but that can also be solved later on.

[github-actions]

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